//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > CUSTOM VECTORS

p
    |  Word vectors let you import knowledge from raw text into your model. The
    |  knowledge is represented as a table of numbers, with one row per term in
    |  your vocabulary. If two terms are used in similar contexts, the algorithm
    |  that learns the vectors should assign them
    |  #[strong rows that are quite similar], while words that are used in
    |  different contexts will have quite different values. This lets you use
    |  the row-values assigned to the words as a kind of dictionary, to tell you
    |  some things about what the words in your text mean.

p
    |  Word vectors are particularly useful for terms which
    |  #[strong aren&apos;t well represented in your labelled training data].
    |  For instance, if you're doing named entity recognition, there will always
    |  be lots of names that you don't have examples of. For instance, imagine
    |  your training data happens to contain some examples of the term
    |  "Microsoft", but it doesn't contain any examples of the term "Symantec".
    |  In your raw text sample, there are plenty of examples of both terms, and
    |  they're used in similar contexts. The word vectors make that fact
    |  available to the entity recognition model. It still won't see examples of
    |  "Symantec" labelled as a company. However, it'll see that "Symantec" has
    |  a word vector that usually corresponds to company terms, so it can
    |  #[strong make the inference].

p
    |  In order to make best use of the word vectors, you want the word vectors
    |  table to cover a #[strong very large vocabulary]. However, most words are
    |  rare, so most of the rows in a large word vectors table will be accessed
    |  very rarely, or never at all. You can usually cover more than
    |  #[strong 95% of the tokens] in your corpus with just
    |  #[strong a few thousand rows] in the vector table. However, it's those
    |  #[strong 5% of rare terms] where the word vectors are
    |  #[strong most useful]. The problem is that increasing the size of the
    |  vector table produces rapidly diminishing returns in coverage over these
    |  rare terms.

+h(3, "custom-vectors-coverage") Optimising vector coverage
    +tag-new(2)

p
    |  To help you strike a good balance between coverage and memory usage,
    |  spaCy's #[+api("vectors") #[code Vectors]] class lets you map
    |  #[strong multiple keys] to the #[strong same row] of the table. If
    |  you're using the #[+api("cli#vocab") #[code spacy vocab]] command to
    |  create a vocabulary, pruning the vectors will be taken care of
    |  automatically. You can also do it manually in the following steps:

+list("numbers")
    +item
        |  Start with a #[strong word vectors model] that covers a huge
        |  vocabulary. For instance, the
        |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]] model
        |  provides 300-dimensional GloVe vectors for over 1 million terms of
        |  English.

    +item
        |  If your vocabulary has values set for the #[code Lexeme.prob]
        |  attribute, the lexemes will be sorted by descending probability to
        |  determine which vectors to prune. Otherwise, lexemes will be sorted
        |  by their order in the #[code Vocab].

    +item
        |  Call #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]] with
        |  the number of vectors you want to keep.

+code.
    nlp = spacy.load('en_vectors_web_lg')
    n_vectors = 105000  # number of vectors to keep
    removed_words = nlp.vocab.prune_vectors(n_vectors)

    assert len(nlp.vocab.vectors) &lt;= n_vectors  # unique vectors have been pruned
    assert nlp.vocab.vectors.n_keys &gt; n_vectors  # but not the total entries

p
    |  #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]] reduces the
    |  current vector table to a given number of unique entries, and returns a
    |  dictionary containing the removed words, mapped to #[code (string, score)]
    |  tuples, where #[code string] is the entry the removed word was mapped
    |  to, and #[code score] the similarity score between the two words.

+code("Removed words").
    {
        'Shore': ('coast', 0.732257),
        'Precautionary': ('caution', 0.490973),
        'hopelessness': ('sadness', 0.742366),
        'Continous': ('continuous', 0.732549),
        'Disemboweled': ('corpse', 0.499432),
        'biostatistician': ('scientist', 0.339724),
        'somewheres': ('somewheres', 0.402736),
        'observing': ('observe', 0.823096),
        'Leaving': ('leaving', 1.0)
    }

p
    |  In the example above, the vector for "Shore" was removed and remapped
    |  to the vector of "coast", which is deemed about 73% similar. "Leaving"
    |  was remapped to the vector of "leaving", which is identical.

+h(3, "custom-vectors-add") Adding vectors
    +tag-new(2)

p
    |  spaCy's new #[+api("vectors") #[code Vectors]] class greatly improves the
    |  way word vectors are stored, accessed and used. The data is stored in
    |  two structures:

+list
    +item
        |  An array, which can be either on CPU or #[+a("#gpu") GPU].

    +item
        |  A dictionary mapping string-hashes to rows in the table.

p
    |  Keep in mind that the #[code Vectors] class itself has no
    |  #[+api("stringstore") #[code StringStore]], so you have to store the
    |  hash-to-string mapping separately. If you need to manage the strings,
    |  you should use the #[code Vectors] via the
    |  #[+api("vocab") #[code Vocab]] class, e.g. #[code vocab.vectors]. To
    |  add vectors to the vocabulary, you can use the
    |  #[+api("vocab#set_vector") #[code Vocab.set_vector]] method.

+code("Adding vectors").
    from spacy.vocab import Vocab

    vector_data = {u'dog': numpy.random.uniform(-1, 1, (300,)),
                   u'cat': numpy.random.uniform(-1, 1, (300,)),
                   u'orange': numpy.random.uniform(-1, 1, (300,))}

    vocab = Vocab()
    for word, vector in vector_data.items():
        vocab.set_vector(word, vector)

+h(3, "custom-loading-glove") Loading GloVe vectors
    +tag-new(2)

p
    |  spaCy comes with built-in support for loading
    |  #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
    |  a directory. The #[+api("vectors#from_glove") #[code Vectors.from_glove]]
    |  method assumes a binary format, the vocab provided in a
    |  #[code vocab.txt], and the naming scheme of
    |  #[code vectors.{size}.[fd].bin]. For example:

+aside-code("Directory structure", "yaml").
    └── vectors
        ├── vectors.128.f.bin  # vectors file
        └── vocab.txt          # vocabulary

+table(["File name", "Dimensions", "Data type"])
    +row
        +cell #[code vectors.128.f.bin]
        +cell 128
        +cell float32

    +row
        +cell #[code vectors.300.d.bin]
        +cell 300
        +cell float64 (double)

+code.
    nlp = spacy.load('en')
    nlp.vocab.vectors.from_glove('/path/to/vectors')

p
    |  If your instance of #[code Language] already contains vectors, they will
    |  be overwritten. To create your own GloVe vectors model package like
    |  spaCy's #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]],
    |  you can call #[+api("language#to_disk") #[code nlp.to_disk]], and then
    |  package the model using the #[+api("cli#package") #[code package]]
    |  command.

+h(3, "custom-loading-other") Loading other vectors
    +tag-new(2)

p
    |  You can also choose to load in vectors from other sources, like the
    |  #[+a("https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md") fastText vectors]
    |  for 294 languages, trained on Wikipedia. After reading in the file,
    |  the vectors are added to the #[code Vocab] using the
    |  #[+api("vocab#set_vector") #[code set_vector]] method.

+github("spacy", "examples/vectors_fast_text.py")

+h(3, "custom-similarity") Using custom similarity methods

p
    |  By default, #[+api("token#vector") #[code Token.vector]] returns the
    |  vector for its underlying #[+api("lexeme") #[code Lexeme]], while
    |  #[+api("doc#vector") #[code Doc.vector]] and
    |  #[+api("span#vector") #[code Span.vector]] return an average of the
    |  vectors of their tokens. You can customise these
    |  behaviours by modifying the #[code doc.user_hooks],
    |  #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
    |  dictionaries.

+infobox
    |  For more details on #[strong adding hooks] and #[strong overwriting] the
    |  built-in #[code Doc], #[code Span] and #[code Token] methods, see the
    |  usage guide on #[+a("/usage/processing-pipelines#user-hooks") user hooks].
